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#Azure Data Engineer With AI

Azure Data Engineer is a prominent job role responsible for design of Data Warehouses (DWH). This trending job stream involves Extraction (E) of data from various sources, perform data mashup and Transformations (T) and Loading the data (L) into Warehouse and Lakehouse platforms. With AI & CoPilot we can implement this ETL & DWH with ease !

Modules of Azure Data Engineer

Training Schedules

S NoTime (IST, Mon - Fri)Start Date
16 AM - 7 AMAug 5th
28 PM - 9 PMAug 18th
Fabric Data Engineer With AI

Azure Data Engineer
With AI Contents:

Module 1 : Microsoft SQL (TSQL)

Ch 1: SQL SERVER INTRODUCTION

  • Database Introduction
  •  Types of Databases
  •  Need for & ETL, DWH
  •  BI Implementations
  •  SQL Server Advantages
  •  Version, Editions of MSSQL
  •  Data Analyst Job Roles

Ch 2: SQL SERVER INSTALLATIONS

  • SQL Server 2019, 2017
  • SSMS Tools Installation
  • Database Engine (OLTP)
  • SCM, Configuration Tools
  • Instance Types, Uses
  • Authentication Modes
  • Collation, File Stream

Ch 3: SQL BASICS – 1

  • Need for Databases, Tables
  • Need for SQL Commands
  • DDL, DML & DQL Statements
  • Database Creation @ GUI
  • Data Operations @ GUI
  • Session ID, SQL Context
  • DB, Tables, Data @ SQL

Ch 4: SQL BASICS – 2

  • DDL Variants in MSSQL
  • DML Variants in MSSQL
  • INSERT & INSERT INTO
  • SELECT & SELECT INTO
  • Basic Operators in SQL
  • Special Operators in MSSQL
  • ALTER, ADD, TRUNCATE, DROP

Ch 5: Data Imports, Schemas

  • Data Imports with Excel
  •  ORDER BY & UNION
  • UNION ALL For Sorting Data
  •  Creating, Using Schemas
  •  Real-world Banking Database
  •  Table Migrations @ Schemas
  •  2 Part, 3 Part & 4 Part Naming

Ch 6 : Constraints, Index Basics

  • Need for Constraints, Keys
  •  NULL, NOT NULL, UNIQUE
  •  Primary Key & Foreign Key
  •  RDBMS and ER Models
  •  Identity Property, Default
  •  Clustered Index, Primary Key
  •  Non Clustered Index, Unique

Ch 7: Joins & Views Basics

  • JOINS: Purpose. Inner Joins
  • Left / Right / Full Outer Joins
  • Cross Joins, Query Tuning
  • Creating & Using Views
  • DML, SELECT with Views
  • RLS : WITH CHECK OPTION
  • System Views & Metadata

Ch 8: Functions(UDF), Data Types

  • Using Functions in MSSQL
  •  Scalar Value Functions
  • Inline & Multiline Functions
  • Date & Time Functions
  • String, Aggregate Functions
  • Data Types : Integer, Char, Bit
  • SQL Variant, Timestamp, Date

Ch 9: Stored Procedures,Models

  • Stored Procedures & Usage
  • Creating, Testing Procedures
  • Encryption, Deferred Names
  • SPs for Validations, Analysis
  • System SPs, Recompilation
  • Normal Forms & Types
  • Data Models, Self-References

Ch 10: Triggers, Temp Tables

  • Need for Triggers
  • DDL & DML Triggers
  • Using Memory Tables
  • Data Replication, Automation
  • Local & Global Temp Tables
  • Testing & Using Temp Tables
  • SELECT .. INTO & Bulk Loads

Ch 11: DB Architecture, Locks

  • Planning VLDBs : Files, Sizing
  • Filegroups, Extents & Types
  • Log Files : VLF, Mini LSN
  •  Table Location, Performance
  • Schemas, Transfer, Synonyms
  • Transactions Types, Lock Hint
  •  Query Blocking Scenarios

Ch 12 : Cursors & CTEs, Links

  • Cursors : Realtime Use
  • Fetch & Access Cursor Rows
  • CTEs for SELECT, DML
  • CTEs: Scenarios & Tuning
  • Linked Servers, Remote Joins
  • Linked Servers: MSDTC, RPC
  • Tuning Remote Queries

Ch 13: Merge, Upsert & Rank

  • Need for Merge in ETL
  • Incremental Loads with SQL
  • MERGE and RANK Functions
  • Window Functions, Partition
  • Identify, Remove Duplicates

Ch 14: Grouping & Cube

  • Group By & HAVING
  • Cube, Rollup & Grouping
  • Joins with Group By
  • 3 Table, 4 Table Joins
  • Query Execution Order

Ch 15: Self Joins, Excel Analysis

  • Self Joins & Self References
  •  UNION, UNION ALL
  •  Sub Queries with Joins
  •  IIF, CASE, EXISTS Statements
  •  Excel Analytics, Pivot Reports

Module 2: Azure Data Engineer

Ch 1: ETL, DWH Introduction

  • Database Introduction Data Warehouse (DWH)
  • Data Engineering Work Flow
  • Cloud Concepts: IaaS, PaaS
  • SaaS & Azure Cloud Concepts
  • Azure Resources & Groups
  • Storage, ETL, IoT Resources

Ch 2: Azure Intro, Azure SQL

  • Azure SQL Server, SQL DBA
  • Azure SQL Database (OLTP)
  • Azure SQL Pool (DWH)
  • Connections from SSMS Tool
  • Connections from ADS Tool
  • Pause / Resume SQL Pool
  • Source Data Configurations

Ch 3: Azure Synapse (DWH)

  • Synapse Pool Architecture
  • Control Node, Compute Node
  • DMS & Partitioned Tables
  • Creating Tables with TSQL
  • Distributions: RR, Hash, Repl
  • Big Data Loads with TQL
  • Important DMFs & DMVs

Ch 4: Azure Data Factory (ADF)

  • Need for ADF & Pipelines
  • Linked Services & IRs
  • Datasets, Pipelines, Triggers
  • Copy Data Activity & CDT
  • Data Loads Pipelines, DTUs
  • Pipeline Monitoring, Edits

Ch 5: ADF Incremental Loads – 1

  • File Incremental Loads
  • Storage Account, Data Lake
  • Binary Copy, Schema Drift
  • Staging Concept in ADF
  • DOCP, Logging & Consistency
  • Polybase Concept & Tuning

Ch 6: ADF Incremental Loads – 2

  • Implement SCD with ADF
  • Self-Hosted IR: Realtime Use
  • On-premise Data: Incr Loads
  • Copy Method: Upsert, Keys
  • Staging & ADF Optimizations
  • Pipeline Runs, Activity IDs

Ch 7: ADF Data Flow – 1

  • Data Flow Transformations
  • Spark Clusters for Debugging
  • Optimized Clusters, Preview
  • Conditional Split, SELECT
  • Sort, Union Transformations
  • Pipelines with Data Flow

Ch 8: ADF Data Flow – 2

  • Working with Multiple Tables
  • Join Transform, Broadcast
  • Row Filters, Column Filters
  • Surrogate Keys, Derived Cols
  • ETL Loads Dates, Sink Options
  • Aggregated Data Loads

Ch 9: ADF Data Flow – 3

  • Pivot Transformation
  • Group By & Pivot Keys
  • Column Pattern, Deduplicate
  • Lookup, Cached Lookup
  • Tuning Transformations
  • Tuning Data Flow, Spark

Ch 10: Synapse Analytics – 1

  • Azure Synapse Analytics
  • Dedicated SQL Pools
  • TSQL: Stored Procedures
  • Synapse Pipelines, Tuning
  • SP Activity in Pipelines, Jobs
  • Comparing ADF & Synapse

Ch 11: Synapse Analytics – 2

  • Serverless Pools in Synapse
  • TSQL Scripts with Serverless
  • ADLS Data Imports & ELT
  • Synapse Aggregation, Analytics
  • Synapse Optimizations
  • Synapse Security & Logins

Ch 12: Synapse Analytics – 3

  • Apache Spark Pool & Usage
  • Synapse Analytics with Pools
  • PySpark Staging, Aggregations
  • Spark Queries & Python ETL
  • Python Notebooks, Pipelines
  • Integrating Python with DWH

Ch 13: Parameters, SCD & ETL

  • ADF Templates in Realtime
  • Table Incremental Loads
  • Control Tables, Watermarks
  • Pipeline Parameters, SPs
  • Dynamic Data Sets, SCD

Ch 14: CDC @ ETL, ELT & Tuning

  • Using CDC in ADF
  • Control Tables (CT): Upserts
  • Handling Inserts, Updates
  • SCD Type 1 & Type 2
  • ADF, Synapse: Limitations

Ch 15: Azure Intro & Storage

  • Storage, ETL, IoT Resources
  • Azure Storage Components
  • Azure Storage Account, HNS
  • Azure Data Lake Storage
  • Azure Storage Explorer Tool
  • Storage Explorer Config
  •  Storage Account Properties

Ch 16: Azure Storage Operations

  • BLOB Storage: Containers
  • Storage Browser, Explorer
  • File & Folder Uploads, Edits
  • Azure Tables: Row Key
  • Partition Key, Timestamp
  • Use Cases of BLOB Storage
  • Use Cases of Azure Tables

17: Azure Storage Security

  • Realtime use of Keys
  • Access Keys & Admin Access
  • SAS Keys Generation, Ips
  • Creating, Using Entra Users
  • Azure AD Users, Groups
  • IAM & RBAC with Entra Users
  • ACLs and ADLS Security

Ch 18: Azure SQL DB Migrations

  • On-Premise SQL DB bacpac
  • Azure SQL Deployment
  • Azure Storage from SSMS
  • Azure SQL DB Migration
  • Migration Verifications
  • Testing Migrations in SQL

Ch 19: Azure Stream Analytics

  • Azure IoT Hubs & Devices
  • APIs with Connection Strings
  • Azure Steam Analytic Jobs
  • Inputs, Outputs, SAQL Query
  • LIVE Feed: JSON, AVRO Files
  • Watermark & LIVE Stats

Ch 20: Azure Stream Analytics

  • Azure IoT Hubs & Devices
  • APIs with Connection Strings
  • Azure Steam Analytic Jobs
  • Inputs, Outputs, SAQL Query
  • LIVE Feed: JSON, AVRO Files
  • Watermark & LIVE Stats

Ch 21: Azure Key Vaults, Alerts

  • Azure Encryptions @ REST
  • Azure Key Vaults & Keys
  • SMK & CMK Encryptions
  • Azure Metrics: Ingress
  • Egress, E2E Latency Issues
  • Performance Tuning Options

Ch 22: Azure Storage Optimization

  • BLOB Types & Content Types
  • Hot, Cool, Cold, Archive Types
  • Creating, Using Access Policies
  • Immutable Storage, Rotation
  • Containerization, Indexing
  • Replication: LRS, ZRS, RA-GRS

Ch 23: Azure Pricing, Functions

  • Azure Logic Apps: Usage
  • Log Apps Usage in ETL
  • Snapshots, Azure Functions
  • Azure Functions Realtime Use
  • ETL & DWH with Functions
  • Azure Resource Pricing

Ch 24: Azure Big Data & Spark

  • Azure Big Data & Spark
  • Azure ETL & DWH Databases
  • Azure Spark, HIVE Metastore
  • Azure Databricks Service
  • Spark Cluster (Personal)
  • Unity Catalog & Azure VM

Ch 25: Spark Cluster Operations

  • DBFS: Flat File Imports
  • Table Conversions using GUI
  • Spark Clusters: Table Creations
  •  Basic Transformations in Spark
  • SQL Notebooks: Creation
  • Default DB Queries, Cloning

Ch 26: Python & PySpark, ETL

  • Python Fundamentals
  • Python Data frames: ETL
  • Python for Big Data, Pandas
  • Python Notebooks, Views
  • Aggregated Loads to Spark
  • Spark DB Creations, Tables

Ch 27: PySpark & ADLS, Widgets

  • Creating Spark Databases
  • Spark Tables, Catalog Info
  • PySpark with ADLS Storage
  • Using Widgets for ADLS Keys
  • PySpark Variables & Widgets
  • Using Variables in Functions
  • Spark SQL with Control Text
  • Using Variables in Spark SQL

Ch 28: ADB Jobs, Delta Tables

  • Azure Databrick Jobs
  • Azure Workflows & Tasks
  • Notebook Schedule Options
  •  Continuous Jobs, Notifications
  • Delta Tables & Data Cleansing
  • SCD (Merge Into), Contact, etc.
  • Creating, Using Data frames
  • Multi Data frame Joins

Ch 29: Scala Notebooks & ETL

  • Scala Notebooks: Purpose
  • Aggregated Data Loads
  • Incremental Data Loads
  • Widgets & Jobs with Scala
  • Python Versus Scala
  • Converting Python to Scala
  • JVM Benefits, SQL DB Conn”
  • SQL DB Loads with Scala

Ch 30: Databricks Architecture

  • Azure Databricks Services
  • Cluster Components & DBFS
  • RDD, DAG, Photon, Spotlight
  • Spark Partitioned Tables
  • Cluster Manager: Spark Jobs
  • Databricks Runtime (DBR)
  • Databricks Security
  • Workspace Security
  • Notebook & Job Security

Ch 31: Medallion Architecture

  • Medallion Architecture in ETL
  • DWH Data Loads & Incr Loads
  • Bronze, Silver & Gold Data
  • Processing Raw Data Files
  • Data Cleansing, Formatting
  • Aggregation Advantages
  • DBES & Node Architecture
  • Unity Catalog Concept
  • LUNs and Unity Catalog

Ch 32: Delta LIVE Tables (DLT)

  • Creating Delta LIVE Tables
  • DLT Pipelines in ETL, DWH
  • Automated Incr Loads
  • Control Tables, Timestamp
  • SCD Type 1 with DLT
  • SCD Type 2 with DLT
  • Automated Merge Into Stmt
  • Delta Tables Vs DLT
  • Merge Into Vs DLT Pipeline

Module 3: Azure AI & CoPilot

Ch 1 : Fundamental AI Concepts

  • AI: Artificial Intelligence
  • Real-time Implementation
  • Understand Computer Vision
  • Understand Natural Language Processing
  • Document Intelligence and Knowledge Mining
  • Understand Generative AI
  • Challenges and Risks with AI
  • Understand Responsible AI

Ch 2: Fundamentals of Machine Learning

  • Machine Learning Introduction
  • Machine Learning Components
  • Types of Machine Learning
  • Regression, Binary Classification; Multiclass Classification
  • Clustering, Deep Learning
  • Azure Machine Learning

Ch 3 : Fundamentals of Azure AI services

  • AI Services on Azure platform
  • Create Azure AI Service Resources
  • Use Azure AI services
  • Understand Authentication for Azure AI services
  • Exercise – Explore Azure AI Services

Ch 4 : Computer Vision

  • Images and image processing
  • Machine learning for computer vision
  • Azure AI Vision
  • Exercise – Analyze images in Vision Studio

Ch 5 : Natural Language Processing

  • Understand Text Analytics
  • Text Analysis in Azure
  • Exercise – Analyze text with Language Studio

Ch 6 : Document Intelligence and Knowledge Mining

  • Introduction to Document Intelligence
  • Knowledge Mining
  • Explore capabilities of document intelligence
  • Receipt Analysis on Azure
  • Exercise – Extract from data in Document Intelligence Studio

Ch 7 : Generative AI

  • What is generative AI?
  • What are language models?
  • Using language models
  • What are copilots?
  • Considerations for Copilot prompts
  • Extending and developing copilots
  • Exercise – Explore Microsoft Copilot

Ch 8 : Generative AI in Azure

  • Generative AI – Capabilities within AI in Azure
  • Azure Implementation of Gen AI
  • Processing Images, Codes and more

Ch 9 : AI 900 Exam Guidance

  • Describe Artificial Intelligence workloads and considerations
  • Describe fundamental principles of machine learning on Azure
  • Describe features of computer vision workloads on Azure
  • Describe features of Natural Language Processing (NLP) workloads on Azure
  • Describe features of generative AI workloads on Azure

Ch 10 : Azure AI with Data Analytics – 1

  • Implementing AI in Cloud
  • Co-Pilot Concepts in Big Data
  • AI with Azure
  • AI with Azure SQL Database
  • Automated Query Tuning Concepts (OLTP)

Ch 11 : Azure AI with Data Analytics – 2

  • AI with Power BI
  • CoPilot with Power BI – Power Query
  • CoPilot with Power BI – Cloud
  • CoPilot with Power BI – DAX

Ch 12 : Azure AI with Azure Data Engineering – 3

  • AI with Azure Storage Account
  • ADLS Concepts and AI Implementations
  • AI Search Service with ADLS
  • Text Data Handling with AI

SQL SCHOOL

24x7 LIVE Online Server (Lab) with Real-time Databases.
Course includes ONE Real-time Project.

Azure Data Engineer With AI Training FAQ's

What is Azure Data Engineer with AI Job Role?

An Azure Data Engineer with AI specialization is a modern data professional responsible for designing, building, and maintaining end-to-end data solutions in Azure that integrate AI and machine learning capabilities. This role combines traditional data engineering tasks like data ingestion, transformation, storage, and performance tuning with advanced AI integrations such as Azure Cognitive Services, OpenAI APIs, Azure ML, and SynapseML to support intelligent analytics and predictive applications.

What are the Job Roles of an Azure Data Engineer with AI?

💼 Top Job Roles:

1️⃣ Build and manage scalable data pipelines using Azure Data Factory, Synapse & Data Lake
2️⃣ Integrate AI APIs (OpenAI, Azure Cognitive Services, Azure ML) into data workflows
3️⃣ Implement data models, warehouses, and real-time data solutions
4️⃣ Develop intelligent dashboards, semantic layers, and custom ML integrations
5️⃣ Ensure security, governance, and compliance of AI-powered data systems
6️⃣ Collaborate with data scientists, BI developers, and business stakeholders and more..!

What does our Azure Data Engineer with AI Training course contain?

The course is carefully curated with below module:
👉🏻Module 1: Microsoft SQL (TSQL)
👉🏻Module 2: Azure Data Engineer with AI
👉🏻Module 3: Azure AI, Co-Pilot

Who can join this course?

  •  Freshers interested in cloud BI and analytics
  • SQL/BI professionals expanding to AWS BI tools
  • ETL developers moving to AWS cloud solutions
  • Data analysts aiming for AWS BI certifications
  • Anyone looking to build enterprise BI solutions on AWS

No prior coding experience is required. All concepts are taught from scratch

What training modes are available?

Option 1:        LIVE Online Training  (100% Interactive, step by step, assignments)

Option 2:        Self Paced Videos (100% practical, step by step with concept wise assignments)

You may choose any one of these options, same curriculum!

I (Trainer) shall be available for doubts and clarifications, assignment check and review.

Why should I choose SQL School for Azure Data Engineer With AI training?

👉🏻 Every session is Practical, Step by Step with Concept wise FAQs !!

👉🏻 100% results with on-time practice.  Daily Tasks for every session.

👉🏻 Concept wise tasks be submitted before next class for Job Waiters / Starters.

👉🏻 Concept wise tasks due for submission by Weekends for Working Professionals.

Why Choose SQL School

  • 100% Real-Time and Practical
  • ISO 9001:2008 Certified
  • Concept wise FAQs
  • TWO Real-time Case Studies, One Project
  • Weekly Mock Interviews
  • 24/7 LIVE Server Access
  • Realtime Project FAQs
  • Course Completion Certificate
  • Placement Assistance
  • Job Support
  • Realtime Project Solution
  • MS Certification Guidance